Calibration, sharpness and the weighting of experts in a linear opinion pool
Linear opinion pools are the most common form of aggregating the probabilistic judgments of multiple experts. Here, the performance of such an aggregation is examined in terms of the calibration and sharpness of the component judgments. The performance is measured through the average quadratic score...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Hora, Stephen C [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Rechteinformationen: |
Nutzungsrecht: © Springer Science+Business Media New York 2015 |
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| Schlagwörter: |
Operations Research/Decision Theory |
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| Übergeordnetes Werk: |
Enthalten in: Annals of operations research - Dordrecht, The Netherlands : Springer Nature B.V., 1984, 229(2015), 1, Seite 429-450 |
|---|---|
| Übergeordnetes Werk: |
volume:229 ; year:2015 ; number:1 ; pages:429-450 |
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| DOI / URN: |
10.1007/s10479-015-1846-0 |
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OLC1956854932 |
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| 520 | |a Linear opinion pools are the most common form of aggregating the probabilistic judgments of multiple experts. Here, the performance of such an aggregation is examined in terms of the calibration and sharpness of the component judgments. The performance is measured through the average quadratic score of the aggregate. Trade-offs between calibration and sharpness are examined and an expression for the optimal weighting of two dependent experts in a linear combination is given. Circumstances where one expert would be disqualified are investigated. Optimal weights for the multiple, dependent experts are found through a concave quadratic program. | ||
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Linear opinion pools are the most common form of aggregating the probabilistic judgments of multiple experts. Here, the performance of such an aggregation is examined in terms of the calibration and sharpness of the component judgments. The performance is measured through the average quadratic score of the aggregate. Trade-offs between calibration and sharpness are examined and an expression for the optimal weighting of two dependent experts in a linear combination is given. Circumstances where one expert would be disqualified are investigated. Optimal weights for the multiple, dependent experts are found through a concave quadratic program. |
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Linear opinion pools are the most common form of aggregating the probabilistic judgments of multiple experts. Here, the performance of such an aggregation is examined in terms of the calibration and sharpness of the component judgments. The performance is measured through the average quadratic score of the aggregate. Trade-offs between calibration and sharpness are examined and an expression for the optimal weighting of two dependent experts in a linear combination is given. Circumstances where one expert would be disqualified are investigated. Optimal weights for the multiple, dependent experts are found through a concave quadratic program. |
| abstract_unstemmed |
Linear opinion pools are the most common form of aggregating the probabilistic judgments of multiple experts. Here, the performance of such an aggregation is examined in terms of the calibration and sharpness of the component judgments. The performance is measured through the average quadratic score of the aggregate. Trade-offs between calibration and sharpness are examined and an expression for the optimal weighting of two dependent experts in a linear combination is given. Circumstances where one expert would be disqualified are investigated. Optimal weights for the multiple, dependent experts are found through a concave quadratic program. |
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| title_short |
Calibration, sharpness and the weighting of experts in a linear opinion pool |
| url |
http://dx.doi.org/10.1007/s10479-015-1846-0 http://search.proquest.com/docview/1680388122 |
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| author2 |
Kardeş, Erim |
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| doi_str |
10.1007/s10479-015-1846-0 |
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2024-07-03T22:13:53.358Z |
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